/*
 * Artificial Intelligence for Humans
 * Volume 2: Nature Inspired Algorithms
 * Java Version
 * http://www.aifh.org
 * http://www.jeffheaton.com
 *
 * Code repository:
 * https://github.com/jeffheaton/aifh
 *
 * Copyright 2014 by Jeff Heaton
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * For more information on Heaton Research copyrights, licenses
 * and trademarks visit:
 * http://www.heatonresearch.com/copyright
 */
package com.heatonresearch.aifh.learning

/**
 * This interface is the base for all Encog Machine Learning methods.  It
 * defines very little, other than the fact that a subclass is a Machine
 * Learning Method.  A MLMethod is an algorithm that accepts data and
 * provides some sort of insight into it.  This could be a neural network,
 * support vector machine, clustering algorithm, or something else entirely.
 * <p/>
 * Many MLMethods must be trained by a MLTrain object before they are useful.
 */
trait MLMethod {
  /**
   * @return The long term memory for the algorithm.  This is usually weights or other coefficients.
   */
  def getLongTermMemory: Array[Double]
}